Tutorial: Big Data Analytics: Concepts, Technologies, and Applications
Tutorial: Big Data Analytics: Concepts, Technologies, and Applications
I. INTRODUCTION
Big data and analytics are hot topics in both the popular and business press. Articles in publications like the New
York Times, the Wall Street Journal, and Financial Times, as well as books like Super Crunchers [Ayers, 2007],
Competing on Analytics [Davenport and Harris, 2007], and Analytics at Work [Davenport, Harris and Morison, 2010]
have spread the word about the potential value of big data and analytics.
Today, many organizations are collecting, storing, and analyzing massive amounts of data. This data is commonly
referred to as “big data” because of its volume, the velocity with which it arrives, and the variety of forms it takes. Big
data is creating a new generation of decision support data management. Businesses are recognizing the potential
value of this data and are putting the technologies, people, and processes in place to capitalize on the opportunities.
A key to deriving value from big data is the use of analytics. Collecting and storing big data creates little value; it is
only data infrastructure at this point. It must be analyzed and the results used by decision makers and organizational
processes in order to generate value.
Big data and analytics are intertwined, but analytics is not new. Many analytic techniques, such as regression
analysis, simulation, and machine learning, have been available for many years. Even the value in analyzing
unstructured data such as e-mail and documents has been well understood. What is new is the coming together of
advances in computer technology and software, new sources of data (e.g., social media), and business opportunity.
This confluence has created the current interest and opportunities in big data analytics. It is even spawning a new
area of practice and study called “data science” that encompasses the techniques, tools, technologies, and
processes for making sense out of big data.
Big data is creating new jobs and changing existing ones. Gartner [2012] predicts that by 2015 the need to support
big data will create 4.4 million IT jobs globally, with 1.9 million of them in the U.S. For every IT job created, an
additional three jobs will be generated outside of IT. Big data is also creating a high demand for people who can
analyze and use big data. A 2011 study by the McKinsey Global Institute predicts that by 2018 the U.S. alone will
face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and
analysts to analyze big data and make decisions [Manyika, Chui, Brown, Bughin, Dobbs, Roxburgh, and Byers,
2011]. Because companies are seeking people with big data skills, many universities are offering new courses,
certificates, and degree programs to provide students with the needed skills. Vendors such as IBM are helping
educate faculty and students through their university support programs.
At a high level, the requirements for organizational success with big data analytics are the same as those for
business intelligence (BI) in general [Williams, 2004]. At a deeper level, however, there are many nuances that are
important and need to be considered by organizations that are getting into big data analytics. For example,
organizational culture, data architecture, analytical tools, and personnel issues must be considered. Of particular
interest to information technology (IT) professionals are the new technologies, platforms, and approaches that are
being used to store and analyze big data. They aren’t your mother’s BI architecture [Watson, 2012].
Governments and companies are able to integrate personal data from numerous sources and learn much of what
you do, where you go, who your friends are, and what your preferences are. Although this leads to better service
(and profits for companies), it also raises privacy concerns [Clemons, Wilson, Barnett, Jin and Matt, 2014]. There
are few legal restrictions on what big data companies such as Facebook and Google can do with the data they
collect.
In this tutorial, we first consider the nature and sources of big data. Next, we look at the history of analytics, the
various kinds of analytics, and how they are used with big data. Starbucks, Chevron, U.S. Xpress, and Target are
used to illustrate various uses of big data analytics. Current research is documenting the benefits of big data and
provides a compelling argument for its use. The requirements for being successful with big data are discussed and
illustrated, including establishing a clear business need; having strong, committed sponsorship; alignment between
the business and analytics strategies; a fact-based decision-making culture; a strong data infrastructure; the right
analytical tools; and users, analysts, and data scientists skilled in the use of big data analytics. Special attention is
given to the technologies, platforms, and approaches for storing and analyzing big data. Privacy concerns about the
use of big data are also explored.
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